Covid-19 Project in Python

Table of Contents

1. Importing Libraries and Dataset/Observing the Dataframe

2. Modifying the Dataset

• 2a. Splitting the Dataframe
• 2b. Cleaning/Manipulating the two new Covid Dataframes
  • i. Checking for and Removing Nulls
  • ii. Creating Month, Day, Year columns/Changing Datatypes

3. Global Numbers - Total Covid Cases vs Total Covid Deaths

4. Total Covid Cases/Deaths by Continent

5. Covid Infection Rates

Note: The dataset was preprocessed in Microsoft Excel to add the total_cases column as

as it was not available in the original dataset. The total_cases column tabulates the total

number of cases over time as they are added in the new_cases column for each location in the dataset

1. Importing Libraries and Dataset/Observing the Dataframe

# IMPORT LIBRARIES
# PROJECT DATASET FOUND AT https://ourworldindata.org/covid-deaths*
# * ORIGINAL SOURCE AND RAW DATA FOUND AT 
# COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University/
# JHU CSSE COVID-19 DATA - Located at https://github.com/CSSEGISandData/COVID-19


import pandas as pd
import numpy as np
from datetime import date
pd.set_option('display.max_rows',150)
pd.options.mode.chained_assignment = None


import plotly.express as px
import plotly.graph_objects as go

# IMPORT DATASET

covid_datadf = pd.read_csv(r'C:\Users\jksmi\Downloads\master_covid_data.csv')

# OBSERVE DATASET

covid_datadf

	iso_code	continent	location	date	population	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	AFG	Asia	Afghanistan	2/24/2020	39835428.0	5.0	5.0	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
1	AFG	Asia	Afghanistan	2/25/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
2	AFG	Asia	Afghanistan	2/26/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
3	AFG	Asia	Afghanistan	2/27/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
4	AFG	Asia	Afghanistan	2/28/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
138722	ZWE	Africa	Zimbabwe	12/4/2021	15092171.0	138523.0	1082.0	669.571	4709.0	1.0	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
138723	ZWE	Africa	Zimbabwe	12/5/2021	15092171.0	139046.0	523.0	727.857	4710.0	1.0	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
138724	ZWE	Africa	Zimbabwe	12/6/2021	15092171.0	139046.0	0.0	688.571	4710.0	0.0	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
138725	ZWE	Africa	Zimbabwe	12/7/2021	15092171.0	141601.0	2555.0	996.571	4713.0	3.0	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
138726	ZWE	Africa	Zimbabwe	12/8/2021	15092171.0	150628.0	9027.0	2184.429	4720.0	7.0	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN

138727 rows × 67 columns

covid_datadf.describe()

	population	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	total_cases_per_million	new_cases_per_million	new_cases_smoothed_per_million	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
count	1.377390e+05	1.310000e+05	130994.000000	129954.000000	1.196660e+05	119862.000000	129954.000000	130328.000000	130323.000000	129288.000000	...	93274.000000	91928.000000	60278.000000	108801.000000	128970.000000	119877.000000	4894.000000	4894.000000	4894.000000	4894.000000
mean	1.568339e+08	2.180052e+06	8535.869185	8541.670343	5.305418e+04	182.800988	167.866251	21307.046606	90.895441	90.728166	...	10.589385	32.754137	50.839891	3.028244	73.262089	0.726055	33989.159706	9.265480	16.417652	843.997485
std	7.269739e+08	1.237652e+07	44427.334924	43803.864608	2.716035e+05	865.220373	812.301657	35035.087686	214.386916	181.142618	...	10.502004	13.517123	31.817432	2.454183	7.528847	0.150018	95064.788868	17.389516	31.751464	1253.396483
min	4.700000e+01	0.000000e+00	-74347.000000	-6223.000000	1.000000e+00	-1918.000000	-232.143000	0.001000	-3125.829000	-272.971000	...	0.100000	7.700000	1.188000	0.100000	53.280000	0.394000	-31959.400000	-27.350000	-95.920000	-1728.844404
25%	2.015490e+06	2.613000e+03	3.000000	10.571000	8.500000e+01	0.000000	0.143000	454.614750	0.326000	1.649000	...	1.900000	21.600000	20.859000	1.300000	67.940000	0.602000	-137.100000	-0.857500	-0.530000	-38.187501
50%	9.749625e+06	3.012450e+04	105.000000	131.429000	7.980000e+02	2.000000	2.000000	3518.864000	11.401000	16.187500	...	6.300000	31.400000	49.839000	2.400000	74.620000	0.744000	2867.650000	5.550000	7.025000	390.200482
75%	3.734479e+07	2.825198e+05	1099.000000	1159.143000	6.975500e+03	22.000000	18.857000	27440.095500	85.506000	95.657000	...	19.300000	41.300000	83.241000	4.000000	78.740000	0.845000	22007.800000	13.980000	22.787500	1409.343556
max	7.874966e+09	2.678298e+08	908289.000000	827220.000000	5.279200e+06	18007.000000	14703.286000	253218.290000	8620.690000	3544.004000	...	44.000000	78.100000	100.000000	13.800000	86.750000	0.957000	984309.300000	121.160000	373.940000	7270.553623

8 rows × 62 columns

# CHECK THE DATA TYPES FOR THE COLUMNS

covid_datadf.dtypes

iso_code                                       object
continent                                      object
location                                       object
date                                           object
population                                    float64
total_cases                                   float64
new_cases                                     float64
new_cases_smoothed                            float64
total_deaths                                  float64
new_deaths                                    float64
new_deaths_smoothed                           float64
total_cases_per_million                       float64
new_cases_per_million                         float64
new_cases_smoothed_per_million                float64
total_deaths_per_million                      float64
new_deaths_per_million                        float64
new_deaths_smoothed_per_million               float64
reproduction_rate                             float64
icu_patients                                  float64
icu_patients_per_million                      float64
hosp_patients                                 float64
hosp_patients_per_million                     float64
weekly_icu_admissions                         float64
weekly_icu_admissions_per_million             float64
weekly_hosp_admissions                        float64
weekly_hosp_admissions_per_million            float64
new_tests                                     float64
total_tests                                   float64
total_tests_per_thousand                      float64
new_tests_per_thousand                        float64
new_tests_smoothed                            float64
new_tests_smoothed_per_thousand               float64
positive_rate                                 float64
tests_per_case                                float64
tests_units                                    object
total_vaccinations                            float64
people_vaccinated                             float64
people_fully_vaccinated                       float64
total_boosters                                float64
new_vaccinations                              float64
new_vaccinations_smoothed                     float64
total_vaccinations_per_hundred                float64
people_vaccinated_per_hundred                 float64
people_fully_vaccinated_per_hundred           float64
total_boosters_per_hundred                    float64
new_vaccinations_smoothed_per_million         float64
new_people_vaccinated_smoothed                float64
new_people_vaccinated_smoothed_per_hundred    float64
stringency_index                              float64
population_density                            float64
median_age                                    float64
aged_65_older                                 float64
aged_70_older                                 float64
gdp_per_capita                                float64
extreme_poverty                               float64
cardiovasc_death_rate                         float64
diabetes_prevalence                           float64
female_smokers                                float64
male_smokers                                  float64
handwashing_facilities                        float64
hospital_beds_per_thousand                    float64
life_expectancy                               float64
human_development_index                       float64
excess_mortality_cumulative_absolute          float64
excess_mortality_cumulative                   float64
excess_mortality                              float64
excess_mortality_cumulative_per_million       float64
dtype: object

2. Modifying the Dataset

# THE ORIGINAL DATASET WILL BE SPLIT INTO TWO (1. covid_deaths, 2. covid_vaccinations). IN ORDER TO DO THIS, I WILL
# PARSE AND APPEND THE 'covid_datadf' DATAFRAME USING THE INDEX NUMBERS OF THE COLUMNS

# FIRST I WILL NEED TO OBTAIN AND DISPLAY THE INDEX NUMBERS OF THE COLUMNS IN THE DATAFRAME
# CREATE A FUNCTION THAT WILL GET THE INDEX LOCATION OF THE COLUMN FOR EVERY COLUMN FOR THE DATAFRAME THAT IS PASSED

def column_index(df):
    for c in df.columns:
        if c in df.columns:
            df.columns.get_loc(c)
        print(df.columns.get_loc(c),c)

# NOW TO CALL 'covid_column_index' FUNCTION

column_index(covid_datadf)

0 iso_code
1 continent
2 location
3 date
4 population
5 total_cases
6 new_cases
7 new_cases_smoothed
8 total_deaths
9 new_deaths
10 new_deaths_smoothed
11 total_cases_per_million
12 new_cases_per_million
13 new_cases_smoothed_per_million
14 total_deaths_per_million
15 new_deaths_per_million
16 new_deaths_smoothed_per_million
17 reproduction_rate
18 icu_patients
19 icu_patients_per_million
20 hosp_patients
21 hosp_patients_per_million
22 weekly_icu_admissions
23 weekly_icu_admissions_per_million
24 weekly_hosp_admissions
25 weekly_hosp_admissions_per_million
26 new_tests
27 total_tests
28 total_tests_per_thousand
29 new_tests_per_thousand
30 new_tests_smoothed
31 new_tests_smoothed_per_thousand
32 positive_rate
33 tests_per_case
34 tests_units
35 total_vaccinations
36 people_vaccinated
37 people_fully_vaccinated
38 total_boosters
39 new_vaccinations
40 new_vaccinations_smoothed
41 total_vaccinations_per_hundred
42 people_vaccinated_per_hundred
43 people_fully_vaccinated_per_hundred
44 total_boosters_per_hundred
45 new_vaccinations_smoothed_per_million
46 new_people_vaccinated_smoothed
47 new_people_vaccinated_smoothed_per_hundred
48 stringency_index
49 population_density
50 median_age
51 aged_65_older
52 aged_70_older
53 gdp_per_capita
54 extreme_poverty
55 cardiovasc_death_rate
56 diabetes_prevalence
57 female_smokers
58 male_smokers
59 handwashing_facilities
60 hospital_beds_per_thousand
61 life_expectancy
62 human_development_index
63 excess_mortality_cumulative_absolute
64 excess_mortality_cumulative
65 excess_mortality
66 excess_mortality_cumulative_per_million

2a. Splitting the Dataframe

# NOW THAT THE COLUMN INDEXES ARE KNOWN, THE 'covid_deaths' AND 'covid_vaccinations' DATAFRAMES CAN BE
# CREATED USING THE RANGE FUNCTION

# FIRST, CREATE THE 'covid_deaths' DATAFRAME

covid_deaths = covid_datadf.iloc[:,0:26]

# NOW TO TAKE A LOOK AT THE NEWLY CREATED 'covid_deaths' DATAFRAME

covid_deaths

	iso_code	continent	location	date	population	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	new_deaths_smoothed_per_million	reproduction_rate	icu_patients	icu_patients_per_million	hosp_patients	hosp_patients_per_million	weekly_icu_admissions	weekly_icu_admissions_per_million	weekly_hosp_admissions	weekly_hosp_admissions_per_million
0	AFG	Asia	Afghanistan	2/24/2020	39835428.0	5.0	5.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	AFG	Asia	Afghanistan	2/25/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	AFG	Asia	Afghanistan	2/26/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	AFG	Asia	Afghanistan	2/27/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	AFG	Asia	Afghanistan	2/28/2020	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
138722	ZWE	Africa	Zimbabwe	12/4/2021	15092171.0	138523.0	1082.0	669.571	4709.0	1.0	...	0.047	3.09	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
138723	ZWE	Africa	Zimbabwe	12/5/2021	15092171.0	139046.0	523.0	727.857	4710.0	1.0	...	0.047	3.04	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
138724	ZWE	Africa	Zimbabwe	12/6/2021	15092171.0	139046.0	0.0	688.571	4710.0	0.0	...	0.038	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
138725	ZWE	Africa	Zimbabwe	12/7/2021	15092171.0	141601.0	2555.0	996.571	4713.0	3.0	...	0.057	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
138726	ZWE	Africa	Zimbabwe	12/8/2021	15092171.0	150628.0	9027.0	2184.429	4720.0	7.0	...	0.123	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

138727 rows × 26 columns

covid_deaths.columns.tolist()

['iso_code',
 'continent',
 'location',
 'date',
 'population',
 'total_cases',
 'new_cases',
 'new_cases_smoothed',
 'total_deaths',
 'new_deaths',
 'new_deaths_smoothed',
 'total_cases_per_million',
 'new_cases_per_million',
 'new_cases_smoothed_per_million',
 'total_deaths_per_million',
 'new_deaths_per_million',
 'new_deaths_smoothed_per_million',
 'reproduction_rate',
 'icu_patients',
 'icu_patients_per_million',
 'hosp_patients',
 'hosp_patients_per_million',
 'weekly_icu_admissions',
 'weekly_icu_admissions_per_million',
 'weekly_hosp_admissions',
 'weekly_hosp_admissions_per_million']

# NOW TO CREATE THE 'covid_vaccinations' DATAFRAME
# TWO TEMPORARY DATAFRAMES WILL USED TO ACCOMPLISH THIS AS I WILL NEED TO ACCOMPLISH THIS: THE FIRST ('covidtmp1')
# WITH COLUMNS 1-4 FROM THE 'covid_datadf' AND THE SECOND ('covidtmp2') WITH COLUMNS 26-66 FROM THE 'covid_datadf'
# THEN I WILL COMBINE (CONCAT) THE TWO TOGETHER TO CREATE THE 'covid_vaccinations' DATAFRAME

# NOW TO CREATE THE FIRST TEMPORARY DATAFRAME('covidtmp1')

covidtmp1 = covid_datadf.iloc[:,0:4]

# NOW TO CHECK THE 'covidtmp1' DATAFRAME TO MAKE SURE IT HAS THE NECESSARY COLUMNS

covidtmp1.columns

Index(['iso_code', 'continent', 'location', 'date'], dtype='object')

# NOW TO CHECK THE DATA IN THE 'covidtmp1' DATAFRAME

covidtmp1.head()

	iso_code	continent	location	date
0	AFG	Asia	Afghanistan	2/24/2020
1	AFG	Asia	Afghanistan	2/25/2020
2	AFG	Asia	Afghanistan	2/26/2020
3	AFG	Asia	Afghanistan	2/27/2020
4	AFG	Asia	Afghanistan	2/28/2020

# NOW TO CREATE THE SECOND TEMPORARY DATAFRAME('covidtmp2')

covidtmp2 = covid_datadf.iloc[:,26:]

# NOW TO CHECK THE 'covidtmp2' DATAFRAME TO MAKE SURE IT HAS THE NECESSARY COLUMNS

covidtmp2.columns

Index(['new_tests', 'total_tests', 'total_tests_per_thousand',
       'new_tests_per_thousand', 'new_tests_smoothed',
       'new_tests_smoothed_per_thousand', 'positive_rate', 'tests_per_case',
       'tests_units', 'total_vaccinations', 'people_vaccinated',
       'people_fully_vaccinated', 'total_boosters', 'new_vaccinations',
       'new_vaccinations_smoothed', 'total_vaccinations_per_hundred',
       'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred',
       'total_boosters_per_hundred', 'new_vaccinations_smoothed_per_million',
       'new_people_vaccinated_smoothed',
       'new_people_vaccinated_smoothed_per_hundred', 'stringency_index',
       'population_density', 'median_age', 'aged_65_older', 'aged_70_older',
       'gdp_per_capita', 'extreme_poverty', 'cardiovasc_death_rate',
       'diabetes_prevalence', 'female_smokers', 'male_smokers',
       'handwashing_facilities', 'hospital_beds_per_thousand',
       'life_expectancy', 'human_development_index',
       'excess_mortality_cumulative_absolute', 'excess_mortality_cumulative',
       'excess_mortality', 'excess_mortality_cumulative_per_million'],
      dtype='object')

# NOW TO CHECK THE DATA IN THE 'covidtmp2' DATAFRAME

covidtmp2.head()

	new_tests	total_tests	total_tests_per_thousand	new_tests_per_thousand	new_tests_smoothed	new_tests_smoothed_per_thousand	positive_rate	tests_per_case	tests_units	total_vaccinations	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN

5 rows × 41 columns

# NOW TO COMBINE (CONCAT) THE 'covidtmp1' AND 'covidtmp2' DATAFRAME TO CREATE THE 'covid_vaccinations' DATAFRAME

covid_vaccinations = pd.concat([covidtmp1,covidtmp2], axis=1)

# NOW TO CHECK THE NEWLY CREATED 'covid_vaccinations' DATAFRAME TO MAKE SURE IT HAS THE INTENDED COLUMNS

covid_vaccinations.columns.tolist()

['iso_code',
 'continent',
 'location',
 'date',
 'new_tests',
 'total_tests',
 'total_tests_per_thousand',
 'new_tests_per_thousand',
 'new_tests_smoothed',
 'new_tests_smoothed_per_thousand',
 'positive_rate',
 'tests_per_case',
 'tests_units',
 'total_vaccinations',
 'people_vaccinated',
 'people_fully_vaccinated',
 'total_boosters',
 'new_vaccinations',
 'new_vaccinations_smoothed',
 'total_vaccinations_per_hundred',
 'people_vaccinated_per_hundred',
 'people_fully_vaccinated_per_hundred',
 'total_boosters_per_hundred',
 'new_vaccinations_smoothed_per_million',
 'new_people_vaccinated_smoothed',
 'new_people_vaccinated_smoothed_per_hundred',
 'stringency_index',
 'population_density',
 'median_age',
 'aged_65_older',
 'aged_70_older',
 'gdp_per_capita',
 'extreme_poverty',
 'cardiovasc_death_rate',
 'diabetes_prevalence',
 'female_smokers',
 'male_smokers',
 'handwashing_facilities',
 'hospital_beds_per_thousand',
 'life_expectancy',
 'human_development_index',
 'excess_mortality_cumulative_absolute',
 'excess_mortality_cumulative',
 'excess_mortality',
 'excess_mortality_cumulative_per_million']

# NOW TO CHECK THE DATA IN THE 'covid_vaccinations' DATAFRAME

covid_vaccinations.head()

	iso_code	continent	location	date	new_tests	total_tests	total_tests_per_thousand	new_tests_per_thousand	new_tests_smoothed	new_tests_smoothed_per_thousand	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	AFG	Asia	Afghanistan	2/24/2020	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
1	AFG	Asia	Afghanistan	2/25/2020	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
2	AFG	Asia	Afghanistan	2/26/2020	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
3	AFG	Asia	Afghanistan	2/27/2020	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
4	AFG	Asia	Afghanistan	2/28/2020	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN

5 rows × 45 columns

2b. Cleaning/Manipulating the two new Covid Dataframes

i. Checking for and Removing Nulls

# NOW TO CHECK FOR MISSING VALUES WITHIN THE COLUMNS
# CREATE A FUNCTION THAT WILL CHECK THE PERCENTAGE OF MISSING VALUES IN THE COLUMNS FOR THE DATAFRAME PASSED

def check_nulls(df):
    for col in df.columns:
        pct_missing = np.mean((df)[col].isnull())
        print('{} - {}%'.format(col, pct_missing))

# NOW TO CHECK THE PERCENTAGE OF NULL VALUES IN THE 'covid_deaths' DATAFRAME

check_nulls(covid_deaths)

iso_code - 0.0%
continent - 0.06361414865166838%
location - 0.0%
date - 0.0%
population - 0.007121901288141458%
total_cases - 0.05569932313104154%
new_cases - 0.055742573543722564%
new_cases_smoothed - 0.0632393117417662%
total_deaths - 0.1373993526854902%
new_deaths - 0.13598650587124353%
new_deaths_smoothed - 0.0632393117417662%
total_cases_per_million - 0.06054336935131589%
new_cases_per_million - 0.06057941136188341%
new_cases_smoothed_per_million - 0.06804010754935953%
total_deaths_per_million - 0.14214248127617551%
new_deaths_per_million - 0.14072963446192882%
new_deaths_smoothed_per_million - 0.06804010754935953%
reproduction_rate - 0.20992308634944892%
icu_patients - 0.8777238749486401%
icu_patients_per_million - 0.8777238749486401%
hosp_patients - 0.8603300006487562%
hosp_patients_per_million - 0.8603300006487562%
weekly_icu_admissions - 0.9904921176122888%
weekly_icu_admissions_per_million - 0.9904921176122888%
weekly_hosp_admissions - 0.9840982649376113%
weekly_hosp_admissions_per_million - 0.9840982649376113%

# NOW TO CHECK THE PERCENTAGE OF NULL VALUES IN THE 'covid_vaccinations' DATAFRAME

check_nulls(covid_vaccinations)

iso_code - 0.0%
continent - 0.06361414865166838%
location - 0.0%
date - 0.0%
new_tests - 0.5802259113222372%
total_tests - 0.5793681114707303%
total_tests_per_thousand - 0.5793681114707303%
new_tests_per_thousand - 0.5802259113222372%
new_tests_smoothed - 0.4945900942138156%
new_tests_smoothed_per_thousand - 0.4945900942138156%
positive_rate - 0.5240364168474774%
tests_per_case - 0.5288011706445033%
tests_units - 0.47958220101350135%
total_vaccinations - 0.7364031515134041%
people_vaccinated - 0.7480879713393932%
people_fully_vaccinated - 0.769331132367888%
total_boosters - 0.9332069460162765%
new_vaccinations - 0.7806627404903155%
new_vaccinations_smoothed - 0.528152414454288%
total_vaccinations_per_hundred - 0.7364031515134041%
people_vaccinated_per_hundred - 0.7480879713393932%
people_fully_vaccinated_per_hundred - 0.769331132367888%
total_boosters_per_hundred - 0.9332069460162765%
new_vaccinations_smoothed_per_million - 0.528152414454288%
new_people_vaccinated_smoothed - 0.536701579360903%
new_people_vaccinated_smoothed_per_hundred - 0.536701579360903%
stringency_index - 0.18451346889934908%
population_density - 0.09491303062850058%
median_age - 0.13806973408204604%
aged_65_older - 0.14758482487187066%
aged_70_older - 0.1427696122600503%
gdp_per_capita - 0.13252647285676183%
extreme_poverty - 0.4204516784764321%
cardiovasc_death_rate - 0.1367578048973884%
diabetes_prevalence - 0.10768631917362878%
female_smokers - 0.3276435012650746%
male_smokers - 0.3373460105098503%
handwashing_facilities - 0.565491937402236%
hospital_beds_per_thousand - 0.21571864164870574%
life_expectancy - 0.07033237942145365%
human_development_index - 0.13587837983954096%
excess_mortality_cumulative_absolute - 0.9647220800565138%
excess_mortality_cumulative - 0.9647220800565138%
excess_mortality - 0.9647220800565138%
excess_mortality_cumulative_per_million - 0.9647220800565138%

# FOR THE PURPOSES OF THE PROJECT, I WILL REMOVE THE ROWS IN THE 'continent' COLUMN FOR BOTH THE
# 'covid_deaths' AND 'covid_vaccinations' DATAFRAMES WHERE THERE ARE MISSING VALUES
# THIS IS TO HELP WITH OUTPUTS WHEN GROUPING BY 'continent' LATER ON IN THE PROJECT

covid_deaths = covid_deaths[covid_deaths['continent'].notna()]
covid_vaccinations = covid_vaccinations[covid_vaccinations['continent'].notna()]

# NOW TO CHECK THE PERCENTAGE OF MISSING VALUES IN THE 'covid_deaths' DATAFRAME TO SEE IF THE REMOVAL WORKED

check_nulls(covid_deaths)

iso_code - 0.0%
continent - 0.0%
location - 0.0%
date - 0.0%
population - 0.002440301150097766%
total_cases - 0.05943711413219196%
new_cases - 0.05952949146279503%
new_cases_smoothed - 0.0669427722436914%
total_deaths - 0.14509399393388864%
new_deaths - 0.145124786377423%
new_deaths_smoothed - 0.0669427722436914%
total_cases_per_million - 0.05943711413219196%
new_cases_per_million - 0.05952949146279503%
new_cases_smoothed_per_million - 0.0669427722436914%
total_deaths_per_million - 0.14509399393388864%
new_deaths_per_million - 0.145124786377423%
new_deaths_smoothed_per_million - 0.0669427722436914%
reproduction_rate - 0.16150636633770074%
icu_patients - 0.869416945081677%
icu_patients_per_million - 0.869416945081677%
hosp_patients - 0.8508414035195763%
hosp_patients_per_million - 0.8508414035195763%
weekly_icu_admissions - 0.9898461917445459%
weekly_icu_admissions_per_million - 0.9898461917445459%
weekly_hosp_admissions - 0.9830179673908023%
weekly_hosp_admissions_per_million - 0.9830179673908023%

# NOW TO CHECK THE PERCENTAGE OF MISSING VALUES IN THE 'covid_vaccinations' DATAFRAME TO SEE IF THE REMOVAL WORKED

check_nulls(covid_vaccinations)

iso_code - 0.0%
continent - 0.0%
location - 0.0%
date - 0.0%
new_tests - 0.5517082108050685%
total_tests - 0.5507921356099214%
total_tests_per_thousand - 0.5507921356099214%
new_tests_per_thousand - 0.5517082108050685%
new_tests_smoothed - 0.46025465350802913%
new_tests_smoothed_per_thousand - 0.46025465350802913%
positive_rate - 0.49170143646749087%
tests_per_case - 0.4967898877615433%
tests_units - 0.4442271866483965%
total_vaccinations - 0.7503117734907854%
people_vaccinated - 0.7627904112330834%
people_fully_vaccinated - 0.7841988576003449%
total_boosters - 0.9500161660328555%
new_vaccinations - 0.7973087404350973%
new_vaccinations_smoothed - 0.5282828593863066%
total_vaccinations_per_hundred - 0.7503117734907854%
people_vaccinated_per_hundred - 0.7627904112330834%
people_fully_vaccinated_per_hundred - 0.7841988576003449%
total_boosters_per_hundred - 0.9500161660328555%
new_vaccinations_smoothed_per_million - 0.5282828593863066%
new_people_vaccinated_smoothed - 0.5374128188942433%
new_people_vaccinated_smoothed_per_hundred - 0.5374128188942433%
stringency_index - 0.12911271573955752%
population_density - 0.03871379963356992%
median_age - 0.08480238949361826%
aged_65_older - 0.09496389585995596%
aged_70_older - 0.0898215577897184%
gdp_per_capita - 0.0788825422241382%
extreme_poverty - 0.3863681852473403%
cardiovasc_death_rate - 0.08340133331280504%
diabetes_prevalence - 0.052354852119289925%
female_smokers - 0.28725500762112977%
male_smokers - 0.2976166648704408%
handwashing_facilities - 0.5412618743360379%
hospital_beds_per_thousand - 0.16772643993164077%
life_expectancy - 0.012463241520530861%
human_development_index - 0.08246216378500716%
excess_mortality_cumulative_absolute - 0.9623254453357146%
excess_mortality_cumulative - 0.9623254453357146%
excess_mortality - 0.9623254453357146%
excess_mortality_cumulative_per_million - 0.9623254453357146%

ii. Creating Month, Day, Year columns/Changing Datatypes

# CHECKING THE DATA TYPES OF BOTH THE 'covid_deaths' AND 'covid_vaccinations' DATAFRAMES, BOTH THE 'date' COLUMNS
# ARE OF object TYPE; I WILL CHANGE TO datatime DATA TYPE AND THE CREATE 3 NEW COLUMNS ('month', 'day', 'year') TO
# BOTH 'covid_deaths' AND 'covid_vaccinations' DATAFRAMES, AGAIN TO HELP WITH OUTPUT WHEN GROUPING BY LATER IN PROJECT

covid_deaths.dtypes

iso_code                               object
continent                              object
location                               object
date                                   object
population                            float64
total_cases                           float64
new_cases                             float64
new_cases_smoothed                    float64
total_deaths                          float64
new_deaths                            float64
new_deaths_smoothed                   float64
total_cases_per_million               float64
new_cases_per_million                 float64
new_cases_smoothed_per_million        float64
total_deaths_per_million              float64
new_deaths_per_million                float64
new_deaths_smoothed_per_million       float64
reproduction_rate                     float64
icu_patients                          float64
icu_patients_per_million              float64
hosp_patients                         float64
hosp_patients_per_million             float64
weekly_icu_admissions                 float64
weekly_icu_admissions_per_million     float64
weekly_hosp_admissions                float64
weekly_hosp_admissions_per_million    float64
dtype: object

covid_vaccinations.dtypes

iso_code                                       object
continent                                      object
location                                       object
date                                           object
new_tests                                     float64
total_tests                                   float64
total_tests_per_thousand                      float64
new_tests_per_thousand                        float64
new_tests_smoothed                            float64
new_tests_smoothed_per_thousand               float64
positive_rate                                 float64
tests_per_case                                float64
tests_units                                    object
total_vaccinations                            float64
people_vaccinated                             float64
people_fully_vaccinated                       float64
total_boosters                                float64
new_vaccinations                              float64
new_vaccinations_smoothed                     float64
total_vaccinations_per_hundred                float64
people_vaccinated_per_hundred                 float64
people_fully_vaccinated_per_hundred           float64
total_boosters_per_hundred                    float64
new_vaccinations_smoothed_per_million         float64
new_people_vaccinated_smoothed                float64
new_people_vaccinated_smoothed_per_hundred    float64
stringency_index                              float64
population_density                            float64
median_age                                    float64
aged_65_older                                 float64
aged_70_older                                 float64
gdp_per_capita                                float64
extreme_poverty                               float64
cardiovasc_death_rate                         float64
diabetes_prevalence                           float64
female_smokers                                float64
male_smokers                                  float64
handwashing_facilities                        float64
hospital_beds_per_thousand                    float64
life_expectancy                               float64
human_development_index                       float64
excess_mortality_cumulative_absolute          float64
excess_mortality_cumulative                   float64
excess_mortality                              float64
excess_mortality_cumulative_per_million       float64
dtype: object

# FIRST TO CHANGE THE 'date' COLUMN IN THE 'covid_deaths' DATAFRAME TO datetime

covid_deaths['date'] = pd.to_datetime(covid_deaths['date'])

# CHECKING TO SEE IF THE CHANGE WORKED

covid_deaths.dtypes

iso_code                                      object
continent                                     object
location                                      object
date                                  datetime64[ns]
population                                   float64
total_cases                                  float64
new_cases                                    float64
new_cases_smoothed                           float64
total_deaths                                 float64
new_deaths                                   float64
new_deaths_smoothed                          float64
total_cases_per_million                      float64
new_cases_per_million                        float64
new_cases_smoothed_per_million               float64
total_deaths_per_million                     float64
new_deaths_per_million                       float64
new_deaths_smoothed_per_million              float64
reproduction_rate                            float64
icu_patients                                 float64
icu_patients_per_million                     float64
hosp_patients                                float64
hosp_patients_per_million                    float64
weekly_icu_admissions                        float64
weekly_icu_admissions_per_million            float64
weekly_hosp_admissions                       float64
weekly_hosp_admissions_per_million           float64
dtype: object

# NOW TO ADD THE 'month', 'day', AND 'year' COLUMNS TO THE 'covid_deaths' DATAFRAME

covid_deaths['month'] = covid_deaths['date'].dt.month
covid_deaths['day'] = covid_deaths['date'].dt.day
covid_deaths['year'] = covid_deaths['date'].dt.year

# NOW TO SEE IF COLUMNS WERE SUCCESSFULLY ADDED

covid_deaths

	iso_code	continent	location	date	population	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	icu_patients_per_million	hosp_patients	hosp_patients_per_million	weekly_icu_admissions	weekly_icu_admissions_per_million	weekly_hosp_admissions	weekly_hosp_admissions_per_million	month	day	year
0	AFG	Asia	Afghanistan	2020-02-24	39835428.0	5.0	5.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	24	2020
1	AFG	Asia	Afghanistan	2020-02-25	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	25	2020
2	AFG	Asia	Afghanistan	2020-02-26	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	26	2020
3	AFG	Asia	Afghanistan	2020-02-27	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	27	2020
4	AFG	Asia	Afghanistan	2020-02-28	39835428.0	5.0	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	28	2020
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
138722	ZWE	Africa	Zimbabwe	2021-12-04	15092171.0	138523.0	1082.0	669.571	4709.0	1.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12	4	2021
138723	ZWE	Africa	Zimbabwe	2021-12-05	15092171.0	139046.0	523.0	727.857	4710.0	1.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12	5	2021
138724	ZWE	Africa	Zimbabwe	2021-12-06	15092171.0	139046.0	0.0	688.571	4710.0	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12	6	2021
138725	ZWE	Africa	Zimbabwe	2021-12-07	15092171.0	141601.0	2555.0	996.571	4713.0	3.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12	7	2021
138726	ZWE	Africa	Zimbabwe	2021-12-08	15092171.0	150628.0	9027.0	2184.429	4720.0	7.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12	8	2021

129902 rows × 29 columns

# NOW TO CHANGE THE 'date' COLUMN IN THE 'covid_vaccinations' DATAFRAME TO datetime

covid_vaccinations['date'] = pd.to_datetime(covid_vaccinations['date'])

# CHECKING TO SEE IF THE CHANGE WORKED

covid_vaccinations.dtypes

iso_code                                              object
continent                                             object
location                                              object
date                                          datetime64[ns]
new_tests                                            float64
total_tests                                          float64
total_tests_per_thousand                             float64
new_tests_per_thousand                               float64
new_tests_smoothed                                   float64
new_tests_smoothed_per_thousand                      float64
positive_rate                                        float64
tests_per_case                                       float64
tests_units                                           object
total_vaccinations                                   float64
people_vaccinated                                    float64
people_fully_vaccinated                              float64
total_boosters                                       float64
new_vaccinations                                     float64
new_vaccinations_smoothed                            float64
total_vaccinations_per_hundred                       float64
people_vaccinated_per_hundred                        float64
people_fully_vaccinated_per_hundred                  float64
total_boosters_per_hundred                           float64
new_vaccinations_smoothed_per_million                float64
new_people_vaccinated_smoothed                       float64
new_people_vaccinated_smoothed_per_hundred           float64
stringency_index                                     float64
population_density                                   float64
median_age                                           float64
aged_65_older                                        float64
aged_70_older                                        float64
gdp_per_capita                                       float64
extreme_poverty                                      float64
cardiovasc_death_rate                                float64
diabetes_prevalence                                  float64
female_smokers                                       float64
male_smokers                                         float64
handwashing_facilities                               float64
hospital_beds_per_thousand                           float64
life_expectancy                                      float64
human_development_index                              float64
excess_mortality_cumulative_absolute                 float64
excess_mortality_cumulative                          float64
excess_mortality                                     float64
excess_mortality_cumulative_per_million              float64
dtype: object

# NOW TO ADD THE 'month', 'day', AND 'year' COLUMNS TO THE 'covid_deaths' DATAFRAME

covid_vaccinations['month'] = covid_vaccinations['date'].dt.month
covid_vaccinations['day'] = covid_vaccinations['date'].dt.day
covid_vaccinations['year'] = covid_vaccinations['date'].dt.year

# NOW TO SEE IF COLUMNS WERE SUCCESSFULLY ADDED

covid_vaccinations

	iso_code	continent	location	date	new_tests	total_tests	total_tests_per_thousand	new_tests_per_thousand	new_tests_smoothed	new_tests_smoothed_per_thousand	...	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million	month	day	year
0	AFG	Asia	Afghanistan	2020-02-24	NaN	NaN	NaN	NaN	NaN	NaN	...	0.5	64.83	0.511	NaN	NaN	NaN	NaN	2	24	2020
1	AFG	Asia	Afghanistan	2020-02-25	NaN	NaN	NaN	NaN	NaN	NaN	...	0.5	64.83	0.511	NaN	NaN	NaN	NaN	2	25	2020
2	AFG	Asia	Afghanistan	2020-02-26	NaN	NaN	NaN	NaN	NaN	NaN	...	0.5	64.83	0.511	NaN	NaN	NaN	NaN	2	26	2020
3	AFG	Asia	Afghanistan	2020-02-27	NaN	NaN	NaN	NaN	NaN	NaN	...	0.5	64.83	0.511	NaN	NaN	NaN	NaN	2	27	2020
4	AFG	Asia	Afghanistan	2020-02-28	NaN	NaN	NaN	NaN	NaN	NaN	...	0.5	64.83	0.511	NaN	NaN	NaN	NaN	2	28	2020
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
138722	ZWE	Africa	Zimbabwe	2021-12-04	7099.0	1485499.0	98.428	0.47	5204.0	0.345	...	1.7	61.49	0.571	NaN	NaN	NaN	NaN	12	4	2021
138723	ZWE	Africa	Zimbabwe	2021-12-05	NaN	NaN	NaN	NaN	NaN	NaN	...	1.7	61.49	0.571	NaN	NaN	NaN	NaN	12	5	2021
138724	ZWE	Africa	Zimbabwe	2021-12-06	NaN	NaN	NaN	NaN	NaN	NaN	...	1.7	61.49	0.571	NaN	NaN	NaN	NaN	12	6	2021
138725	ZWE	Africa	Zimbabwe	2021-12-07	NaN	NaN	NaN	NaN	NaN	NaN	...	1.7	61.49	0.571	NaN	NaN	NaN	NaN	12	7	2021
138726	ZWE	Africa	Zimbabwe	2021-12-08	NaN	NaN	NaN	NaN	NaN	NaN	...	1.7	61.49	0.571	NaN	NaN	NaN	NaN	12	8	2021

129902 rows × 48 columns

3. Global Numbers - Total Covid Cases vs Total Covid Deaths

# NOW TO FIND GLOBAL NUMBERS WITHIN THE 'covid_death' DATAFRAME
# FIRST, FIND THE TOTAL NUMBER OF COVID CASES

total_covid_cases = covid_deaths['new_cases'].sum()

total_covid_cases

267229814.0

# NOW TO FIND THE TOTAL NUMBER OF COVID DEATHS

total_covid_deaths = covid_deaths['new_deaths'].sum()

total_covid_deaths

5262258.0

# NOW TO FIND THE DEATH PERCENTAGE

death_percentage = (total_covid_deaths/total_covid_cases)*100

death_percentage

1.9691882134079548

# NOW TO CREATE A 'global_numbers' DATAFRAME FROM THE 'total_covid_cases', 'total_covid_deaths' AND 'death_percentage' VARIABLES

data = [[total_covid_cases,total_covid_deaths,death_percentage]]
global_numbers = pd.DataFrame(data, columns=['total_covid_cases','total_covid_deaths','death_percentage'])

# NOW TO CHECK THE 'global_numbers' DATAFRAME

global_numbers

	total_covid_cases	total_covid_deaths	death_percentage
0	267229814.0	5262258.0	1.969188

4. Total Covid Cases/Deaths by Continent

# NOW TO CHECK THE TOTAL COVID CASES AND DEATHS BY CONTINENT

covid_numbers_continent = covid_deaths.groupby(['continent'])[['new_cases','new_deaths']].sum().reset_index()

covid_numbers_continent

	continent	new_cases	new_deaths
0	Africa	8784938.0	223923.0
1	Asia	81954793.0	1231854.0
2	Europe	78082793.0	1451244.0
3	North America	59140387.0	1179882.0
4	Oceania	324851.0	3397.0
5	South America	38942052.0	1171958.0

# NOW TO VISUALIZE THE TOTAL COVID CASES BY CONTINENT

continent_case_numbers = covid_numbers_continent.sort_values(by='new_cases', ascending=False)
continent_cases_graph = px.bar(continent_case_numbers, x='continent', y='new_cases', title="Total Covid Cases by Continent",labels={'new_cases':'Total Covid Cases', 'continent':'Continent'})

continent_cases_graph.show()

# NOW TO DO THE SAME WITH TOTAL COVID DEATHS BY CONTINENT

continent_death_numbers = covid_numbers_continent.sort_values(by='new_deaths', ascending=False)
continent_death_graph = px.bar(continent_death_numbers, x='continent', y='new_deaths', title="Total Covid Deaths by Continent", labels={'new_deaths':'Total Covid Deaths', 'continent':'Continent'})

continent_death_graph.show()

5. Covid Infection Rates

# CREATE DATAFRAME THAT SHOWS COUNTRIES WITH HIGHEST INFECTION RATE COMPARED TO POPULATION

# HIGHEST INFECTION RATE

highest_infection_rate = covid_deaths.groupby(['iso_code','location','population'])[['total_cases']].agg(['max']).reset_index()
highest_infection_rate

	iso_code	location	population	total_cases
				max
0	ABW	Aruba	107195.0	NaN
1	AFG	Afghanistan	39835428.0	157542.0
2	AGO	Angola	33933611.0	65332.0
3	AIA	Anguilla	15125.0	NaN
4	ALB	Albania	2872934.0	202641.0
...	...	...	...	...
218	WSM	Samoa	200144.0	3.0
219	YEM	Yemen	30490639.0	10047.0
220	ZAF	South Africa	60041996.0	3071064.0
221	ZMB	Zambia	18920657.0	210436.0
222	ZWE	Zimbabwe	15092171.0	150628.0

223 rows × 4 columns

# RESETTING THE MULTI-INDEX OF THE 'highest_infection_rate' DATAFRAME TO A SINGLE INDEX

highest_infection_rate.columns = ["".join(a) for a in highest_infection_rate.columns.to_flat_index()]

# NOW TO FIND AND ADD COLUMN THAT SHOWS THE PERCENTAGE OF POPULATION INFECTED

highest_infection_rate['percent_pop_infected'] = (highest_infection_rate['total_casesmax']) / (highest_infection_rate['population']) * 100

# NOW TO CHECK THE 'highest_infection_rate' DATAFRAME

highest_infection_rate

	iso_code	location	population	total_casesmax	percent_pop_infected
0	ABW	Aruba	107195.0	NaN	NaN
1	AFG	Afghanistan	39835428.0	157542.0	0.395482
2	AGO	Angola	33933611.0	65332.0	0.192529
3	AIA	Anguilla	15125.0	NaN	NaN
4	ALB	Albania	2872934.0	202641.0	7.053451
...	...	...	...	...	...
218	WSM	Samoa	200144.0	3.0	0.001499
219	YEM	Yemen	30490639.0	10047.0	0.032951
220	ZAF	South Africa	60041996.0	3071064.0	5.114860
221	ZMB	Zambia	18920657.0	210436.0	1.112202
222	ZWE	Zimbabwe	15092171.0	150628.0	0.998054

223 rows × 5 columns

# NOW TO GRAPH THE PERCENTAGE OF POPULATION INFECTED BY COUNTRY

fig=px.choropleth(highest_infection_rate, locations='iso_code',color='percent_pop_infected', color_continuous_scale=px.colors.sequential.Sunsetdark)
fig.update_layout(title="Percent Population Infected per Country", height=1000)
fig.update_geos(projection_type="orthographic", showocean=True,oceancolor="deepskyblue", showlakes=True, lakecolor="lightskyblue", bgcolor="lightgray")
fig.show()

# NOW TO LOOK AT INFECTION RATE OVER TIME
# CREATE DATAFRAME THAT HIGHEST INFECTION RATE OVER TIME

# INFECTION RATE OVER TIME

infection_over_time = covid_deaths.groupby(['iso_code','location','population','date'])[['total_cases']].agg(['max']).reset_index()
infection_over_time

	iso_code	location	population	date	total_cases
					max
0	ABW	Aruba	107195.0	2021-03-29	NaN
1	ABW	Aruba	107195.0	2021-03-30	NaN
2	ABW	Aruba	107195.0	2021-03-31	NaN
3	ABW	Aruba	107195.0	2021-04-01	NaN
4	ABW	Aruba	107195.0	2021-04-02	NaN
...	...	...	...	...	...
129580	ZWE	Zimbabwe	15092171.0	2021-12-04	138523.0
129581	ZWE	Zimbabwe	15092171.0	2021-12-05	139046.0
129582	ZWE	Zimbabwe	15092171.0	2021-12-06	139046.0
129583	ZWE	Zimbabwe	15092171.0	2021-12-07	141601.0
129584	ZWE	Zimbabwe	15092171.0	2021-12-08	150628.0

129585 rows × 5 columns

# RESETTING THE MULTI-INDEX OF THE 'highest_infection_rate' DATAFRAME TO A SINGLE INDEX

infection_over_time.columns = ["".join(a) for a in infection_over_time.columns.to_flat_index()]

# NOW TO ADD THE 'percent_pop_infected' COLUMN TO THE 'infection_over_time' DATAFRAME

infection_over_time['percent_pop_infected'] = (infection_over_time['total_casesmax']) / (infection_over_time['population']) * 100

# NOW TO CHECK THE 'infection_over_time' DATAFRAME

infection_over_time

	iso_code	location	population	date	total_casesmax	percent_pop_infected
0	ABW	Aruba	107195.0	2021-03-29	NaN	NaN
1	ABW	Aruba	107195.0	2021-03-30	NaN	NaN
2	ABW	Aruba	107195.0	2021-03-31	NaN	NaN
3	ABW	Aruba	107195.0	2021-04-01	NaN	NaN
4	ABW	Aruba	107195.0	2021-04-02	NaN	NaN
...	...	...	...	...	...	...
129580	ZWE	Zimbabwe	15092171.0	2021-12-04	138523.0	0.917847
129581	ZWE	Zimbabwe	15092171.0	2021-12-05	139046.0	0.921312
129582	ZWE	Zimbabwe	15092171.0	2021-12-06	139046.0	0.921312
129583	ZWE	Zimbabwe	15092171.0	2021-12-07	141601.0	0.938241
129584	ZWE	Zimbabwe	15092171.0	2021-12-08	150628.0	0.998054

129585 rows × 6 columns

# NOW TO GRAPH THE COVID INFECTION RATE OVER TIME

fig = px.line(infection_over_time, x="date", y="percent_pop_infected", color="location")
fig.update_layout(title="Percent Population Infected over Time", xaxis_title=None, yaxis_title="Percent Population Infected", plot_bgcolor="lightgray")
fig.show()

# TO CREATE A CLEANER GRAPH I WILL CREATE A DATAFRAME TO GRAPH ONLY 7 LOCATIONS FROM THE 'infection_over_time' DATAFRAME 
# (United States, Mexico, United Kingdom, UAE, India, Brazil, and South Africa) - 
# NAMING THE DATAFRAME 'graph_locations' 

graph_locations = infection_over_time.query('(location==["United States","Mexico","United Kingdom","United Arab Emirates","India","Brazil","South Africa"])')

# CHECKING THE 'graph_locations' DATAFRAME

graph_locations

	iso_code	location	population	date	total_casesmax	percent_pop_infected
3146	ARE	United Arab Emirates	9991083.0	2020-01-29	4.0	0.000040
3147	ARE	United Arab Emirates	9991083.0	2020-01-30	4.0	0.000040
3148	ARE	United Arab Emirates	9991083.0	2020-01-31	4.0	0.000040
3149	ARE	United Arab Emirates	9991083.0	2020-02-01	4.0	0.000040
3150	ARE	United Arab Emirates	9991083.0	2020-02-02	5.0	0.000050
...	...	...	...	...	...	...
128320	ZAF	South Africa	60041996.0	2021-12-04	3020569.0	5.030760
128321	ZAF	South Africa	60041996.0	2021-12-05	3031694.0	5.049289
128322	ZAF	South Africa	60041996.0	2021-12-06	3038075.0	5.059917
128323	ZAF	South Africa	60041996.0	2021-12-07	3051222.0	5.081813
128324	ZAF	South Africa	60041996.0	2021-12-08	3071064.0	5.114860

4755 rows × 6 columns

# NOW TO GRAPH THE COVID INFECTION RATE OVER TIME FOR THE LOCATIONS IN THE 'graph_locations' DATAFRAME


fig = px.line(graph_locations, x="date", y="percent_pop_infected", color="location")
fig.update_layout(title="Percent Population Infected over Time", xaxis_title=None, yaxis_title="Percent Population Infected", plot_bgcolor="lightgray")
fig.show()